{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b94d6307",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5086f512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 282 ms (started: 2024-07-29 19:01:29 +02:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime\n",
    "\n",
    "from IPython.display import display_html\n",
    "from PIL import Image as PILImage\n",
    "from openpyxl import load_workbook\n",
    "from io import BytesIO\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90eaf8af",
   "metadata": {},
   "source": [
    "### Documents\n",
    "\n",
    "Documents can be either an image or a PDF file. Check README for supported formats."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2594279d",
   "metadata": {},
   "source": [
    "#### Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e7b95fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 610 ms (started: 2024-07-29 19:01:30 +02:00)\n"
     ]
    }
   ],
   "source": [
    "from img2table.document import Image\n",
    "\n",
    "img_path = \"data/tables.png\"\n",
    "\n",
    "# Definition of image from path\n",
    "img_from_path = Image(src=img_path)\n",
    "\n",
    "# Definition of image from bytes\n",
    "with open(img_path, 'rb') as f:\n",
    "    img_bytes = f.read()\n",
    "img_from_bytes = Image(src=img_bytes)\n",
    "\n",
    "# Definition of image from file-like object\n",
    "img_from_file_like = Image(src=BytesIO(img_bytes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083c21e4",
   "metadata": {},
   "source": [
    "#### PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46a763f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0 ns (started: 2024-07-29 19:01:30 +02:00)\n"
     ]
    }
   ],
   "source": [
    "from img2table.document import PDF\n",
    "\n",
    "pdf_path = \"data/tables.pdf\"\n",
    "\n",
    "# Definition of PDF from path\n",
    "# The optional pages argument enables the extraction of table on specific pages of the PDF\n",
    "pdf_from_path = PDF(src=pdf_path, pages=[0, 1])\n",
    "\n",
    "# Definition of PDF from bytes\n",
    "with open(pdf_path, 'rb') as f:\n",
    "    pdf_bytes = f.read()\n",
    "pdf_from_bytes = PDF(src=pdf_bytes)\n",
    "\n",
    "# Definition of PDF from file-like object\n",
    "pdf_from_file_like = PDF(src=BytesIO(pdf_bytes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f85a7f",
   "metadata": {},
   "source": [
    "### OCR instances\n",
    "\n",
    "<code>img2table</code> provides supports for several OCR tools and services :\n",
    "  * Tesseract\n",
    "  * PaddleOCR\n",
    "  * EasyOCR\n",
    "  * docTR\n",
    "  * Surya OCR (<code>python >= 3.10</code>)\n",
    "  * Google Vision\n",
    "  * AWS Textract\n",
    "  * Azure Cognitive Services\n",
    "  \n",
    "For all OCRs except Tesseract, a specific installation is needed. Check README for relevant instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92778211",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xavca\\PycharmProjects\\img2table\\venv\\lib\\site-packages\\torchvision\\models\\_utils.py:135: UserWarning: Using 'weights' as positional parameter(s) is deprecated since 0.13 and may be removed in the future. Please use keyword parameter(s) instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\xavca\\PycharmProjects\\img2table\\venv\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.4 s (started: 2024-07-29 19:01:30 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# Tesseract OCR\n",
    "from img2table.ocr import TesseractOCR\n",
    "\n",
    "tesseract_ocr = TesseractOCR(n_threads=1, lang=\"eng\")\n",
    "\n",
    "# PaddleOCR\n",
    "from img2table.ocr import PaddleOCR\n",
    "\n",
    "paddle_ocr = PaddleOCR(lang=\"en\", kw={\"use_dilation\": True})\n",
    "\n",
    "# EasyOCR\n",
    "from img2table.ocr import EasyOCR\n",
    "\n",
    "easyocr = EasyOCR(lang=[\"en\"], kw={\"gpu\": False})\n",
    "\n",
    "# docTR\n",
    "from img2table.ocr import DocTR\n",
    "\n",
    "doctr = DocTR(detect_language=True, kw={\"detect_orientation\": True})\n",
    "\n",
    "# Google Vision OCR\n",
    "from img2table.ocr import VisionOCR\n",
    "\n",
    "vision_ocr = VisionOCR(api_key=\"***\")\n",
    "\n",
    "# AWS Textract OCR\n",
    "from img2table.ocr import TextractOCR\n",
    "\n",
    "textract_ocr = TextractOCR(aws_access_key_id=\"***\",\n",
    "                           aws_secret_access_key=\"***\",\n",
    "                           aws_session_token=\"***\",\n",
    "                           region=\"eu-west-1\")\n",
    "\n",
    "# Azure Cognitive Services OCR\n",
    "from img2table.ocr import AzureOCR\n",
    "\n",
    "azure_ocr = AzureOCR(endpoint=\"abc.azure.com\",\n",
    "                           subscription_key=\"***\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64139e2e",
   "metadata": {},
   "source": [
    "### Table identification\n",
    "\n",
    "For a document object (image or pdf), the <code>extract_tables</code> method can be called to perform table identification.<br>\n",
    "A list/dict of <code>ExtractedTable</code> objects is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc97c4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 610 ms (started: 2024-07-29 19:01:45 +02:00)\n"
     ]
    }
   ],
   "source": [
    "img = Image(src=\"data/tables.png\")\n",
    "\n",
    "# Extract tables\n",
    "extracted_tables = img.extract_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "148f1e35",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1365x417>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 31 ms (started: 2024-07-29 19:01:45 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# Display extracted tables\n",
    "table_img = cv2.imread(\"data/tables.png\")\n",
    "\n",
    "for table in extracted_tables:\n",
    "    for row in table.content.values():\n",
    "        for cell in row:\n",
    "            cv2.rectangle(table_img, (cell.bbox.x1, cell.bbox.y1), (cell.bbox.x2, cell.bbox.y2), (255, 0, 0), 2)\n",
    "            \n",
    "PILImage.fromarray(table_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229e9a8e",
   "metadata": {},
   "source": [
    "### Table extraction\n",
    "\n",
    "If an ocr instance is provided to the <code>extract_table</code> method, cells content will be infered from the OCR result.<br>\n",
    "A <code>pd.DataFrame</code> representation of the extracted tables can be accessed via the <code>df</code> attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea97f8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [ExtractedTable(title=Example of Data Table 1, bbox=(235, 249, 1442, 543),shape=(5, 4)),\n",
       "  ExtractedTable(title=Example of Data Table 2, bbox=(236, 672, 1452, 972),shape=(5, 4))],\n",
       " 1: [ExtractedTable(title=Example of Data Table 3, bbox=(235, 249, 1442, 543),shape=(5, 4)),\n",
       "  ExtractedTable(title=Example of Data Table 4, bbox=(236, 672, 1452, 972),shape=(5, 4))]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1 s (started: 2024-07-29 19:01:45 +02:00)\n"
     ]
    }
   ],
   "source": [
    "pdf = PDF(src=\"data/tables.pdf\")\n",
    "\n",
    "# Extract tables\n",
    "extracted_tables = pdf.extract_tables(ocr=tesseract_ocr,\n",
    "                                      implicit_rows=False,\n",
    "                                      borderless_tables=False,\n",
    "                                      min_confidence=50)\n",
    "\n",
    "extracted_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3851b0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 1 - Extracted table n°1</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 1<br>\n",
       "                       <b>Bounding box:</b> x1=235, y1=249, x2=1442, y2=543\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample</td>\n",
       "      <td>blue LED value</td>\n",
       "      <td>green LED value</td>\n",
       "      <td>red LED value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clear water</td>\n",
       "      <td>97</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue water</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>green water</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tea water</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 1 - Extracted table n°2</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 2<br>\n",
       "                       <b>Bounding box:</b> x1=236, y1=672, x2=1452, y2=972\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample</td>\n",
       "      <td>blue % transmitted</td>\n",
       "      <td>green % transmitted</td>\n",
       "      <td>red % transmitted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clear water</td>\n",
       "      <td>97/97 = 100%</td>\n",
       "      <td>19/19 = 100%</td>\n",
       "      <td>79/79 = 100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue water</td>\n",
       "      <td>73/97 = 75%</td>\n",
       "      <td>11/19 = 58%</td>\n",
       "      <td>13/79 = 17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>green water</td>\n",
       "      <td>35/97 = 36%</td>\n",
       "      <td>15/19 = 79%</td>\n",
       "      <td>14/79 = 18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tea water</td>\n",
       "      <td>33/97 = 34%</td>\n",
       "      <td>13/19 = 68%</td>\n",
       "      <td>70/79 = 91%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 2 - Extracted table n°1</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 3<br>\n",
       "                       <b>Bounding box:</b> x1=235, y1=249, x2=1442, y2=543\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample</td>\n",
       "      <td>blue LED value</td>\n",
       "      <td>green LED value</td>\n",
       "      <td>red LED value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+ 1 tsp milk</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+1tsp milk + blue</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+1tsp milk +green</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+1tsp milk + tea</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 2 - Extracted table n°2</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 4<br>\n",
       "                       <b>Bounding box:</b> x1=236, y1=672, x2=1452, y2=972\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Water sample</td>\n",
       "      <td>blue % transmitted</td>\n",
       "      <td>green % transmitted</td>\n",
       "      <td>red % transmitted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+ 1 tsp milk</td>\n",
       "      <td>13/13 = 100%</td>\n",
       "      <td>14/14 = 100%</td>\n",
       "      <td>12/12 = 100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+1tsp milk + blue</td>\n",
       "      <td>10/13 = 77%</td>\n",
       "      <td>9/14 = 64%</td>\n",
       "      <td>6/12 = 50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+1tsp milk +green</td>\n",
       "      <td>9/13 = 69%</td>\n",
       "      <td>13/14 = 93%</td>\n",
       "      <td>7/12 = 58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+1tsp milk + tea</td>\n",
       "      <td>7/13 = 54%</td>\n",
       "      <td>9/14 = 69%</td>\n",
       "      <td>9/12 = 75%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 16 ms (started: 2024-07-29 19:01:46 +02:00)\n"
     ]
    }
   ],
   "source": [
    "for page, tables in extracted_tables.items():\n",
    "    for idx, table in enumerate(tables):\n",
    "        display_html(table.html_repr(title=f\"Page {page + 1} - Extracted table n°{idx + 1}\"), raw=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e94d997",
   "metadata": {},
   "source": [
    "### Export to xlsx\n",
    "\n",
    "All tables from a document can be exported in xslx format, with one table per worksheet.<br>\n",
    "Arguments are similar to the <code>extract_tables</code> method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e869f327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.09 s (started: 2024-07-29 19:01:46 +02:00)\n"
     ]
    }
   ],
   "source": [
    "pdf = PDF(src=\"data/tables.pdf\")\n",
    "\n",
    "# Export to file\n",
    "pdf.to_xlsx('data/tables.xlsx',\n",
    "            ocr=tesseract_ocr,\n",
    "            implicit_rows=False,\n",
    "            borderless_tables=False,\n",
    "            min_confidence=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b177bacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worksheet Page 1 - Table 1 : 5 rows, 4 columns\n",
      "Worksheet Page 1 - Table 2 : 5 rows, 4 columns\n",
      "Worksheet Page 2 - Table 1 : 5 rows, 4 columns\n",
      "Worksheet Page 2 - Table 2 : 5 rows, 4 columns\n",
      "time: 31 ms (started: 2024-07-29 19:01:48 +02:00)\n"
     ]
    }
   ],
   "source": [
    "for ws in load_workbook(\"data/tables.xlsx\"):\n",
    "    print(f\"Worksheet {ws.title} : {len(tuple(ws.rows))} rows, {len(tuple(ws.rows)[0])} columns\")"
   ]
  }
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